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Ma ZP, Wang SW, Xue LY, Zhang XD, Zheng W, Zhao YX, Yuan SR, Li GY, Yu YN, Wang JN, Zhang TL. A study on the application of radiomics based on cardiac MR non-enhanced cine sequence in the early diagnosis of hypertensive heart disease. BMC Med Imaging 2024; 24:124. [PMID: 38802736 PMCID: PMC11129462 DOI: 10.1186/s12880-024-01301-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Accepted: 05/15/2024] [Indexed: 05/29/2024] Open
Abstract
BACKGROUND The prevalence of hypertensive heart disease (HHD) is high and there is currently no easy way to detect early HHD. Explore the application of radiomics using cardiac magnetic resonance (CMR) non-enhanced cine sequences in diagnosing HHD and latent cardiac changes caused by hypertension. METHODS 132 patients who underwent CMR scanning were divided into groups: HHD (42), hypertension with normal cardiac structure and function (HWN) group (46), and normal control (NOR) group (44). Myocardial regions of the end-diastolic (ED) and end-systolic (ES) phases of the CMR short-axis cine sequence images were segmented into regions of interest (ROI). Three feature subsets (ED, ES, and ED combined with ES) were established after radiomic least absolute shrinkage and selection operator feature selection. Nine radiomic models were built using random forest (RF), support vector machine (SVM), and naive Bayes. Model performance was analyzed using receiver operating characteristic curves, and metrics like accuracy, area under the curve (AUC), precision, recall, and specificity. RESULTS The feature subsets included first-order, shape, and texture features. SVM of ED combined with ES achieved the highest accuracy (0.833), with a macro-average AUC of 0.941. AUCs for HHD, HWN, and NOR identification were 0.967, 0.876, and 0.963, respectively. Precisions were 0.972, 0.740, and 0.826; recalls were 0.833, 0.804, and 0.863, respectively; and specificities were 0.989, 0.863, and 0.909, respectively. CONCLUSIONS Radiomics technology using CMR non-enhanced cine sequences can detect early cardiac changes due to hypertension. It holds promise for future use in screening for latent cardiac damage in early HHD.
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Affiliation(s)
- Ze-Peng Ma
- Department of Radiology, Affiliated Hospital of Hebei University/ Clinical Medical College, Hebei University, Baoding, 071000, China
- Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, Baoding, 071000, China
| | - Shi-Wei Wang
- College of Quality and Technical Supervision, Hebei University, Baoding, 071002, China
| | - Lin-Yan Xue
- College of Quality and Technical Supervision, Hebei University, Baoding, 071002, China
| | - Xiao-Dan Zhang
- Department of Ultrasound, Affiliated Hospital of Hebei University, 212 Yuhua East Road, Baoding, 071000, China.
| | - Wei Zheng
- College of Electronic and Information Engineering, Hebei University, Baoding, 071002, China
| | - Yong-Xia Zhao
- Department of Radiology, Affiliated Hospital of Hebei University/ Clinical Medical College, Hebei University, Baoding, 071000, China
| | - Shuang-Rui Yuan
- Department of Radiology, Affiliated Hospital of Hebei University/ Clinical Medical College, Hebei University, Baoding, 071000, China
| | - Gao-Yang Li
- Department of Radiology, Affiliated Hospital of Hebei University/ Clinical Medical College, Hebei University, Baoding, 071000, China
| | - Ya-Nan Yu
- Department of Radiology, Affiliated Hospital of Hebei University/ Clinical Medical College, Hebei University, Baoding, 071000, China
| | - Jia-Ning Wang
- Department of Radiology, Affiliated Hospital of Hebei University/ Clinical Medical College, Hebei University, Baoding, 071000, China
| | - Tian-Le Zhang
- Department of Radiology, Affiliated Hospital of Hebei University/ Clinical Medical College, Hebei University, Baoding, 071000, China
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Deng J, Zhou L, Li Y, Yu Y, Zhang J, Liao B, Luo G, Tian J, Zhou H, Tang H. Integration of Cine-cardiac Magnetic Resonance Radiomics and Machine Learning for Differentiating Ischemic and Dilated Cardiomyopathy. Acad Radiol 2024:S1076-6332(24)00195-8. [PMID: 38704286 DOI: 10.1016/j.acra.2024.03.032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2024] [Revised: 03/23/2024] [Accepted: 03/24/2024] [Indexed: 05/06/2024]
Abstract
RATIONALE AND OBJECTIVES This study aims to evaluate the capability of machine learning algorithms in utilizing radiomic features extracted from cine-cardiac magnetic resonance (CMR) sequences for differentiating between ischemic cardiomyopathy (ICM) and dilated cardiomyopathy (DCM). MATERIALS AND METHODS This retrospective study included 115 cardiomyopathy patients subdivided into ICM (n = 64) and DCM cohorts (n = 51). We collected invasive clinical (IC), noninvasive clinical (NIC), and combined clinical (CC) feature subsets. Radiomic features were extracted from regions of interest (ROIs) in the left ventricle (LV), LV cavity (LVC), and myocardium (MYO). We tested 10 classical machine learning classifiers and validated them through fivefold cross-validation. We compared the efficacy of clinical feature-based models and radiomics-based models to identify the superior diagnostic approach. RESULTS In the validation set, the Gaussian naive Bayes (GNB) model outperformed the other models in all categories, with areas under the curve (AUCs) of 0.879 for IC_GNB, 0.906 for NIC_GNB, and 0.906 for CC_GNB. Among the radiomics models, the MYO_LASSOCV_MLP model demonstrated the highest AUC (0.919). In the test set, the MYO_RFECV_GNB radiomics model achieved the highest AUC (0.857), surpassing the performance of the three clinical feature models (IC_GNB: 0.732; NIC_GNB: 0.75; CC_GNB: 0.786). CONCLUSION Radiomics models leveraging MYO images from cine-CMR exhibit promising potential for differentiating ICM from DCM, indicating the significant clinical application scope of such models. CLINICAL RELEVANCE STATEMENT The integration of radiomics models and machine learning methods utilizing cine-CMR sequences enhances the diagnostic capability to distinguish between ICM and DCM, minimizes examination risks for patients, and potentially reduces the duration of medical imaging procedures.
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Affiliation(s)
- Jia Deng
- The First Affiliated Hospital, Department of Radiology, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China (J.D., B.L., G.L., H.Z.); The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China (J.D., Y.L., Y.Y., J.Z., H.T.)
| | - Langtao Zhou
- School of Cyberspace Security, Guangzhou University, Guangzhou 510006, China (L.Z.)
| | - Yueyan Li
- The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China (J.D., Y.L., Y.Y., J.Z., H.T.)
| | - Ying Yu
- The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China (J.D., Y.L., Y.Y., J.Z., H.T.)
| | - Jingjing Zhang
- The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China (J.D., Y.L., Y.Y., J.Z., H.T.)
| | - Bihong Liao
- The First Affiliated Hospital, Department of Radiology, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China (J.D., B.L., G.L., H.Z.)
| | - Guanghua Luo
- The First Affiliated Hospital, Department of Radiology, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China (J.D., B.L., G.L., H.Z.)
| | - Jinwei Tian
- Department of Cardiology, The Second Affiliated Hospital of Harbin Medical University, Harbin 150086, China (J.T.)
| | - Hong Zhou
- The First Affiliated Hospital, Department of Radiology, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China (J.D., B.L., G.L., H.Z.).
| | - Huifang Tang
- The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China (J.D., Y.L., Y.Y., J.Z., H.T.); The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China (H.T.); Clinical Research Center for Myocardial Injury in Hunan Province, Hengyang, Hunan 421001, China (H.T.); Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Huna 421001, China (H.T.)
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Sica G, Rea G, Scaglione M. Editorial for the Special Issue "Cardiothoracic Imaging: Recent Techniques and Applications in Diagnostics". Diagnostics (Basel) 2024; 14:461. [PMID: 38472934 DOI: 10.3390/diagnostics14050461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 01/25/2024] [Accepted: 01/25/2024] [Indexed: 03/14/2024] Open
Abstract
Technology is making giant strides and is increasingly improving the diagnostic imaging of both frequent and rare acute and chronic diseases [...].
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Affiliation(s)
- Giacomo Sica
- Department of Radiology, Azienda Ospedaliera dei Colli, Monaldi Hospital, 80131 Naples, Italy
| | - Gaetano Rea
- Department of Radiology, Azienda Ospedaliera dei Colli, Monaldi Hospital, 80131 Naples, Italy
| | - Mariano Scaglione
- Department of Medicine, Surgery and Pharmacy, University of Sassari, 07100 Sassari, Italy
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Zhang Y, Feng Y, Sun J, Zhang L, Ding Z, Wang L, Zhao K, Pan Z, Li Q, Guo N, Xie X. Fully automated artificial intelligence-based coronary CT angiography image processing: efficiency, diagnostic capability, and risk stratification. Eur Radiol 2024:10.1007/s00330-023-10494-6. [PMID: 38193925 DOI: 10.1007/s00330-023-10494-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 09/10/2023] [Accepted: 10/16/2023] [Indexed: 01/10/2024]
Abstract
OBJECTIVES To prospectively investigate whether fully automated artificial intelligence (FAAI)-based coronary CT angiography (CCTA) image processing is non-inferior to semi-automated mode in efficiency, diagnostic ability, and risk stratification of coronary artery disease (CAD). MATERIALS AND METHODS Adults with indications for CCTA were prospectively and consecutively enrolled at two hospitals and randomly assigned to either FAAI-based or semi-automated image processing using equipment workstations. Outcome measures were workflow efficiency, diagnostic accuracy for obstructive CAD (≥ 50% stenosis), and cardiovascular events at 2-year follow-up. The endpoints included major adverse cardiovascular events, hospitalization for unstable angina, and recurrence of cardiac symptoms. The non-inferiority margin was 3 percentage difference in diagnostic accuracy and C-index. RESULTS In total, 1801 subjects (62.7 ± 11.1 years) were included, of whom 893 and 908 were assigned to the FAAI-based and semi-automated modes, respectively. Image processing times were 121.0 ± 18.6 and 433.5 ± 68.4 s, respectively (p <0.001). Scan-to-report release times were 6.4 ± 2.7 and 10.5 ± 3.8 h, respectively (p < 0.001). Of all subjects, 152 and 159 in the FAAI-based and semi-automated modes, respectively, subsequently underwent invasive coronary angiography. The diagnostic accuracies for obstructive CAD were 94.7% (89.9-97.7%) and 94.3% (89.5-97.4%), respectively (difference 0.4%). Of all subjects, 779 and 784 in the FAAI-based and semi-automated modes were followed for 589 ± 182 days, respectively, and the C-statistic for cardiovascular events were 0.75 (0.67 to 0.83) and 0.74 (0.66 to 0.82) (difference 1%). CONCLUSIONS FAAI-based CCTA image processing significantly improves workflow efficiency than semi-automated mode, and is non-inferior in diagnosing obstructive CAD and risk stratification for cardiovascular events. CLINICAL RELEVANCE STATEMENT Conventional coronary CT angiography image processing is semi-automated. This observation shows that fully automated artificial intelligence-based image processing greatly improves efficiency, and maintains high diagnostic accuracy and the effectiveness in stratifying patients for cardiovascular events. KEY POINTS • Coronary CT angiography (CCTA) relies heavily on high-quality and fast image processing. • Full-automation CCTA image processing is clinically non-inferior to the semi-automated mode. • Full automation can facilitate the application of CCTA in early detection of coronary artery disease.
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Affiliation(s)
- Yaping Zhang
- Radiology Department, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai, 200080, China
| | - Yan Feng
- Radiology Department, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai, 200080, China
| | - Jianqing Sun
- Shukun (Beijing) Technology Co, Ltd, Jinhui Bd, Qiyang Rd, Beijing, 100102, China
| | - Lu Zhang
- Radiology Department, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai, 200080, China
| | - Zhenhong Ding
- Shukun (Beijing) Technology Co, Ltd, Jinhui Bd, Qiyang Rd, Beijing, 100102, China
| | - Lingyun Wang
- Radiology Department, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai, 200080, China
| | - Keke Zhao
- Radiology Department, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai, 200080, China
| | - Zhijie Pan
- Radiology Department, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai, 200080, China
| | - Qingyao Li
- Radiology Department, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai, 200080, China
- Radiology Department, Shanghai General Hospital, University of Shanghai for Science and Technology, Haining Rd.100, Shanghai, 200080, China
| | - Ning Guo
- Shukun (Beijing) Technology Co, Ltd, Jinhui Bd, Qiyang Rd, Beijing, 100102, China
| | - Xueqian Xie
- Radiology Department, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai, 200080, China.
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